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Your Job: We are looking for a PhD student to develop learning-based surrogate models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to
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. This PhD will focus on uncertainty-aware machine learning models, developing and evaluating techniques (e.g., Bayesian and interval neural networks) to quantify model uncertainty and monitor it during
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and/or the CAD/CAM process is a plus. I am proficient in Python and am familiar with data science and machine/deep learning toolkits. As a PhD researcher at KU Leuven, I perform research in a structured
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11.12.2025 Application deadline: 15.02.2026 The Faculty of Science at Tübingen University invites applications for a W3-Professorship in Machine Learning in Physics at the Department of Physics (m/f
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. Machine learning will assist in artifact correction, segmentation, and material classification. By combining experimental imaging, simulation, and data-driven interpretation, this approach will deliver high
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apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular dynamics (MD) simulations
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imaging and machine learning. The main task of the successful candidate will be to help redefine certain traditional criteria of comparative anatomy used in archaeozoology and to establish new criteria
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triggered by colloids, as well as methods for immobilizing these ions. Modern methods of theoretical chemistry (first principles, kinetic Monte Carlo, machine learning) will be applied to investigate
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, traditional planning often fails to capture workload variability, uncertainty, and the complex interaction between product features, labor availability, and machine capacity. Your PhD will address
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continuous programme improvement. Participate in educational initiatives and activities to enhance student learning outcomes. Requirements: A PhD or a Master’s degree (with significant industry experience) in